Conditional Deep Convolutional GAN for the Design of High-Performance Periodic Absorber Metasurfaces

Abstract

This work presents a conditional deep convolutional generative adversarial network (CDCGAN) for the inverse design of high-absorptance periodic metasurfaces (MSs) with different geometries. The conditioning mechanism encodes geometric and spectral parameters. A key innovation is the inclusion of unit cells with different periods to produce fine-tuned responses within the W-band. The period is embedded in the fringe pixels before training and recovered from generated high-resolution images to reconstruct the unit cell. This joint topology–period representation allows both the metallic geometry and lattice scale to be recovered from a single generative output without requiring a separate period-optimization stage. The resulting spectra closely matched the targets when tested at three angles of incidence, thereby demonstrating the method’s effectiveness for high-performance MS design.